Noninvasive targeted modulation of pain circuits with focused ultrasonic waves
Bibliographic record
Abstract
Background: Acupuncture is an effective treatment for patients with depressive symptoms.However, its efficacy remains unclear for patients with a primary diagnosis of depression.We aimed to examine whether scalp acupuncture is an effective augmentation treatment for patients with a primary diagnosis of depression.Methods: 42 patients were recruited and randomly assigned to daily acupuncture treatment (n¼26) for 10 continuous working days, or the control arm without acupuncture treatment.Patients were assessed by blinded raters at pre-treatment, immediately after treatment, one-month, and three-month post-treatment.MontgomeryeÅsberg Depression Rating Scale (MADRS), Quick Inventory of Depressive Symptomatology-Selfreport 16-items (QIDS-SR16), and TCM depression assessment conducted by TCM physicians were used for depressive symptoms, Montreal Cognitive Assessment (MoCA) for cognitive function, and patient self-reported EQ-5D scale for subjective quality of life.Data were analyzed with independent student t-test.Results: Patients in the treatment arm displayed a trend of better improvement of depressive symptoms assessed by QIDS-SR16 [baseline 19.79±1.64vs 19.91±1.83(control vs treatment, mean±SE, p¼0.962); posttreatment 14.23±1.81vs 12.52±1.66;p¼0.508; one-month 17.43±2.86vs 13.79±2.07,p¼0.352; and three-month 21.50±3.23 vs 13.55±2.18,p¼0.076)].There was significant improvement in the Traditional Chinese Medicine Likert scale for depression [baseline 6.69±0.58vs 6.96±0.54(p¼0.740);post-treatment 6±0.47 vs 4.43±0.51(p¼0.045);one-month 6.5±0.71vs 4.26±0.58(p¼0.035); and three-month 7.0±0.82vs 3.82±0.74(p¼0.033)].Better EQ-5D utility scores were observed in the treatment group, though this trend was not observed for cognitive functions assessed by MoCA and depressive symptoms assessed by MADRS.Conclusion: There is a trend of delayed antidepressant effectiveness of scalp acupuncture treatment in reducing phlegm and regulating "Qi" flow for a calm mind in patients with a primary diagnosis of depression.However, data interpretation is limited by the relatively small sample size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".